Linear Mixed Models for Longitudinal Data / Edition 1by Geert Verbeke, Geert Molenberghs
Pub. Date: 06/16/2000
Publisher: Springer New York
This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogenity model, conditional linear mid models).
Table of ContentsA Model for Longitudinal Data.
Exploratory Data Analysis.
Estimation of the Marginal Model.
Inference for the Marginal Model.
Inference for the Random Effects.
Fitting Linear Mixed Models with SAS.
General Guidelines for Model Building.
Exploring Serial Correlation.
Local Influence for the Linear Mixed Model.
The Heterogeneity Model.
Conditional Linear Mixed Models.
Exploring Incomplete Data.
Joint Modeling of Measurements and Missingness.
Simple Missing Data Methods.
Sensitivity Analysis for Selection Models.
Sensitivity Analysis for Models.
How Ignorable is Missing at Random?
The Expectation-Maximization Algorithm.
and post it to your social network
Most Helpful Customer Reviews
See all customer reviews >